scholarly journals Forward and backward evolutionary processes and allele frequency spectrum in a cancer cell population

2017 ◽  
Vol 117 ◽  
pp. 43-50 ◽  
Author(s):  
Hisashi Ohtsuki ◽  
Hideki Innan
Keyword(s):  
Allele Frequency ◽  
Frequency Spectrum ◽  
Cancer Cell ◽  
Cell Population ◽  
Evolutionary Processes ◽  
Allele Frequency Spectrum ◽  
Cancer Cell Population

scholarly journals Allele Frequency Spectrum in a Cancer Cell Population

2017 ◽  
Author(s):  
H. Ohtsuki ◽  
H. Innan
Keyword(s):  
Population Genetics ◽  
Cell Division ◽  
Allele Frequency ◽  
Frequency Spectrum ◽  
Cancer Cell ◽  
Cell Population ◽  
Large Cell ◽  
Parental Cancer ◽  
Allele Frequency Spectrum ◽  
Cancer Cell Population

ABSTRACTA cancer grows from a single cell, thereby constituting a large cell population. In this work, we are interested in how mutations accumulate in a cancer cell population. We provided a theoretical framework of the stochastic process in a cancer cell population and obtained near exact expressions of allele frequency spectrum or AFS (only continuous approximation is involved) from both forward and backward treatments under a simple setting; all cells undergo cell division and die at constant rates, b and d, respectively, such that the entire population grows exponentially. This setting means that once a parental cancer cell is established, in the following growth phase, all mutations are assumed to have no effect on b or d (i.e., neutral or passengers). Our theoretical results show that the difference from organismal population genetics is mainly in the coalescent time scale, and the mutation rate is defined per cell division, not per time unit (e.g., generation). Except for these two factors, the basic logic are very similar between organismal and cancer population genetics, indicating that a number of well established theories of organismal population genetics could be translated to cancer population genetics with simple modifications.

A SIMPLE EVOLUTIONARY MODEL FOR CANCER CELL POPULATION AND ITS IMPLICATIONS ON CANCER THERAPY

2009 ◽  
Vol 23 (25) ◽  
pp. 2999-3011 ◽  
Author(s):  
PENG YAO ◽  
SHUTANG WEN ◽  
BAOSHUN LI ◽  
YUXIAO LI
Keyword(s):  
Cancer Therapy ◽  
Cancer Cell ◽  
Cell Population ◽  
Fitness Landscape ◽  
Evolutionary Model ◽  
Evolutionary Games ◽  
Cellular Interactions ◽  
Malignant Cells ◽  
Quasispecies Model ◽  
Cancer Cell Population

We established a simple evolutionary model based on the cancer stem cell hypothesis. By taking cellular interactions into consideration, we introduced the evolutionary games theory into the quasispecies model. The fitness values are determined by both genotypes and cellular interactions. In the evolutionary model, a cancer cell population can evolve in different patterns. For single peak intrinsic fitness landscape, the evolution pattern can transit with increasing differentiation probability from malignant cells to benign cells in four different modes. For a large enough value of differentiation probability, the evolution is always the case that the malignant cells extinct ultimately, which might give some implications on cancer therapy.

scholarly journals The CEA−/lo colorectal cancer cell population harbors cancer stem cells and metastatic cells

Oncotarget ◽  
2016 ◽  
Vol 7 (49) ◽  
pp. 80700-80715 ◽  
Author(s):  
Chang Yan ◽  
Yibing Hu ◽  
Bo Zhang ◽  
Lei Mu ◽  
Kaiyu Huang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Stem Cells ◽  
Cancer Stem Cells ◽  
Cancer Cell ◽  
Cell Population ◽  
Colorectal Cancer Cell ◽  
Metastatic Cells ◽  
Cancer Cell Population

scholarly journals The impact of phenotypic heterogeneity of tumour cells on treatment and relapse dynamics

2020 ◽  
Author(s):  
Michael Raatz ◽  
Saumil Shah ◽  
Guranda Chitadze ◽  
Monika Brüggemann ◽  
Arne Traulsen
Keyword(s):  
Growth Rate ◽  
Cancer Cell ◽  
Cell Population ◽  
Phenotypic Heterogeneity ◽  
Therapy Options ◽  
Rate Dependent ◽  
Trait Distribution ◽  
Intratumour Heterogeneity ◽  
Treatment Type ◽  
Cancer Cell Population

Intratumour heterogeneity is increasingly recognized as a frequent problem for cancer treatment as it allows for the evolution of resistance against treatment. While cancer genotyping becomes more and more established and allows to determine the genetic heterogeneity, less is known about the phenotypic heterogeneity among cancer cells. We investigate how phenotypic differences can impact the efficiency of therapy options that select on this diversity, compared to therapy options that are independent of the phenotype. We employ the ecological concept of trait distributions and characterize the cancer cell population as a collection of subpopulations that differ in their growth rate. We show in a deterministic model that growth rate-dependent treatment types alter the trait distribution of the cell population, resulting in a delayed relapse compared to a growth rate-independent treatment. Whether the cancer cell population goes extinct or relapse occurs is determined by stochastic dynamics, which we investigate using a stochastic model. Again, we find that relapse is delayed for the growth rate-dependent treatment type, albeit an increased relapse probability, suggesting that slowly growing subpopulations are shielded from extinction. Sequential application of growth rate-dependent and growth rate-independent treatment types can largely increase treatment efficiency and delay relapse. Interestingly, even longer intervals between decisions to change the treatment type may achieve close-to-optimal efficiencies and relapse times. Monitoring patients at regular check-ups may thus provide the temporally resolved guidance to tailor treatments to the changing cancer cell trait distribution and allow clinicians to cope with this dynamic heterogeneity.Author summaryThe individual cells within a cancer cell population are not all equal. The heterogeneity among them can strongly affect disease progression and treatment success. Recent diagnostic advances allow measuring how the characteristics of this heterogeneity change over time. To match these advances, we developed deterministic and stochastic trait-based models that capture important characteristics of the intratumour heterogeneity and allow to evaluate different treatment types that either do or do not interact with this heterogeneity. We focus on growth rate as the decisive characteristic of the intratumour heterogeneity. We find that by shifting the trait distribution of the cancer cell population, the growth rate-dependent treatment delays an eventual relapse compared to the growth rate-independent treatment. As a downside, however, we observe a refuge effect where slower-growing subpopulations are less affected by the growth rate-dependent treatment, which may decrease the likelihood of successful therapy. We find that navigating along this trade-off may be achieved by sequentially combining both treatment types, which agrees qualitatively with current clinical practice. Interestingly, even rather large intervals between treatment changes allow for close-to-optimal treatment results, which again hints towards a practical applicability.

scholarly journals An Efficient Computational Approach for Constructing the Allele Frequency Spectrum of Populations with Arbitrary Complex History

2018 ◽  
Author(s):  
Hua Chen
Keyword(s):  
Allele Frequency ◽  
Frequency Spectrum ◽  
Growth Model ◽  
Scaling Function ◽  
Computational Approach ◽  
Population History ◽  
Logistic Growth ◽  
Allele Frequency Spectrum ◽  
Time Scaling ◽  
Arbitrary Complex

AbstractThe allele frequency spectrum (AFS), or site frequency spectrum, is commonly used to summarize the genomic polymorphism pattern of a sample, which is informative for inferring population history and detecting natural selection. Recently, Chen and Chen (2013) developed a method for analytically deriving the AFS for populations with temporally varying size through the coalescence time-scaling function. However, their approach is only applicable for population history scenarios in which the analytical form of the time-scaling function is tractable. In this paper, we propose a computational approach to extend the method to populations with arbitrary complex history by numerically approximating the time-scaling function. We demonstrate the performance of the approach by constructing the AFS for two population history scenarios: the logistic growth model and the Gompertz growth model, for which the AFS are unavailable with existing approaches.

scholarly journals Finsler-type estimators for the cancer cell population dynamics

2015 ◽  
Vol 98 (112) ◽  
pp. 53-69
Author(s):  
Vladimir Balan ◽  
Jelena Stojanov
Keyword(s):  
Dynamical System ◽  
Population Growth ◽  
Cancer Cell ◽  
Cell Population ◽  
Biological Models ◽  
Cell Population Dynamics ◽  
Finsler Structure ◽  
Cell Population Growth ◽  
Cancer Cell Population ◽  
The Difference

We introduce a Finslerian model related to the classical Garner dynamical system, which models the cancer cell population growth. The Finsler structure is determined by the energy of the deformation field-the difference of the fields, which describe the reduced and the proper biological models. It is shown that a certain locally-Minkowski anisotropic Randers structure, obtained by means of statistical fitting, is able to provide a Zermelo-type drift of the overall cancer cell population growth, which occurs due to significant changes within the cancerous process. The geometric background, the applicative advantages and perspective openings of the constructed geometric structure are discussed.

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